scikit-learn · thomasjpfan · Apr 27, 2020 · Jan 25, 2020 · Apr 25, 2020 · Apr 27, 2020
diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
@@ -834,6 +834,9 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
         For now "auto" (kept for backward compatibiliy) chooses "elkan" but it
         might change in the future for a better heuristic.
 
+        .. versionchanged:: 0.18
+            Added Elkan algorithm
+
     Attributes
     ----------
     cluster_centers_ : ndarray of shape (n_clusters, n_features)

diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py
@@ -141,6 +141,8 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
         ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
         for more details.
 
+        .. versionadded:: 0.18
+
     flatten_transform : bool, default=True
         Affects shape of transform output only when voting='soft'
         If voting='soft' and flatten_transform=True, transform method returns
@@ -232,6 +234,8 @@ def fit(self, X, y, sample_weight=None):
             Note that this is supported only if all underlying estimators
             support sample weights.
 
+            .. versionadded:: 0.18
+
         Returns
         -------
         self : object

diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py
@@ -410,6 +410,8 @@ class RFECV(RFE):
         ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
         for more details.
 
+        .. versionadded:: 0.18
+
     Attributes
     ----------
     n_features_ : int

diff --git a/sklearn/feature_selection/_univariate_selection.py b/sklearn/feature_selection/_univariate_selection.py
@@ -384,6 +384,8 @@ class SelectPercentile(_BaseFilter):
         Default is f_classif (see below "See also"). The default function only
         works with classification tasks.
 
+        .. versionadded:: 0.18
+
     percentile : int, optional, default=10
         Percent of features to keep.
 
@@ -467,6 +469,8 @@ class SelectKBest(_BaseFilter):
         Default is f_classif (see below "See also"). The default function only
         works with classification tasks.
 
+        .. versionadded:: 0.18
+
     k : int or "all", optional, default=10
         Number of top features to select.
         The "all" option bypasses selection, for use in a parameter search.

diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py
@@ -150,6 +150,8 @@ class RANSACRegressor(MetaEstimatorMixin, RegressorMixin,
         If the loss on a sample is greater than the ``residual_threshold``,
         then this sample is classified as an outlier.
 
+        .. versionadded:: 0.18
+
     random_state : int, RandomState instance, default=None
         The generator used to initialize the centers.
         Pass an int for reproducible output across multiple function calls.
@@ -239,6 +241,8 @@ def fit(self, X, y, sample_weight=None):
             raises error if sample_weight is passed and base_estimator
             fit method does not support it.
 
+            .. versionadded:: 0.18
+
         Raises
         ------
         ValueError

diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -227,6 +227,8 @@ def confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None,
     sample_weight : array-like of shape (n_samples,), default=None
         Sample weights.
 
+        .. versionadded:: 0.18
+
     normalize : {'true', 'pred', 'all'}, default=None
         Normalizes confusion matrix over the true (rows), predicted (columns)
         conditions or all the population. If None, confusion matrix will not be
@@ -789,6 +791,8 @@ def matthews_corrcoef(y_true, y_pred, *, sample_weight=None):
     sample_weight : array-like of shape (n_samples,), default=None
         Sample weights.
 
+        .. versionadded:: 0.18
+
     Returns
     -------
     mcc : float
@@ -2156,6 +2160,7 @@ def log_loss(y_true, y_pred, *, eps=1e-15, normalize=True, sample_weight=None,
         If not provided, labels will be inferred from y_true. If ``labels``
         is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
         assumed to be binary and are inferred from ``y_true``.
+
         .. versionadded:: 0.18
 
     Returns

diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py
@@ -881,6 +881,8 @@ def normalized_mutual_info_score(labels_true, labels_pred, *,
 def fowlkes_mallows_score(labels_true, labels_pred, *, sparse=False):
     """Measure the similarity of two clusterings of a set of points.
 
+    .. versionadded:: 0.18
+
     The Fowlkes-Mallows index (FMI) is defined as the geometric mean between of
     the precision and recall::
 

diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
@@ -739,6 +739,8 @@ def split(self, X, y, groups=None):
 class TimeSeriesSplit(_BaseKFold):
     """Time Series cross-validator
 
+    .. versionadded:: 0.18
+
     Provides train/test indices to split time series data samples
     that are observed at fixed time intervals, in train/test sets.
     In each split, test indices must be higher than before, and thus shuffling

diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py
@@ -215,6 +215,8 @@ class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
     simple strategy for extending regressors that do not natively support
     multi-target regression.
 
+    .. versionadded:: 0.18
+
     Parameters
     ----------
     estimator : estimator object

diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py
@@ -66,9 +66,13 @@ class FunctionTransformer(TransformerMixin, BaseEstimator):
     kw_args : dict, optional
         Dictionary of additional keyword arguments to pass to func.
 
+        .. versionadded:: 0.18
+
     inv_kw_args : dict, optional
         Dictionary of additional keyword arguments to pass to inverse_func.
 
+        .. versionadded:: 0.18
+
     Examples
     --------
     >>> import numpy as np

diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -213,6 +213,8 @@ def fit(self, X, y, sample_weight=None):
             samples. If not provided,
             then each sample is given unit weight.
 
+            .. versionadded:: 0.18
+
         Returns
         -------
         self : object
@@ -398,6 +400,8 @@ def fit(self, X, y, sample_weight=None):
             samples. If not provided,
             then each sample is given unit weight.
 
+            .. versionadded:: 0.18
+
         Returns
         -------
         self : object